Although the state-of-the-art IMU data processing pipeline is mature, the current design is still subject to a number of non-negligible error sources.
One of the most critical components is the sensor modeling error introduced in the IMU data pre-processing steps,
which has been extensively studied in the literature, especially the IMU raw data signal processing model [1]}, [2]} and the intrinsic model [3]}, [2]}, [5]}, [6]}.
However, existing sensor models are heavily hand-crafted (e.g., number, order, and arrangement of linear and nonlinear functions in Euclidean and \(SO(3)\)  space), which lack of optimality guarantee and generality across different devices and practices.
